Model-based Performance Analysis Research Articles

A model-based performance analysis of integrated chromatography-ultrafilter separation systems for monoclonal antibody (mAb) manufacturing

Robust and accurate modelling of bioprocess systems offers the opportunity to visualise strategies for rapid-scale up of production platforms from benchtop to industrial scale, but few studies investigate how mechanistic and hybrid models can be used to design integrated bio-manufacturing. This is especially true for downstream purification technologies used to separate mAbs from culture by-products, e.g. host cell proteins. Polishing chromatography (cation-exchange mode) is commonly used to remove these by-products, but optimal bind and elution operation of these columns is complex. Ultrafiltration is typically applied in conjunction with diafiltration post-polishing chromatography in order to obtain suitable final product titers and excipient concentrations. This study presents a combination of dynamic models to probe batch polishing chromatography and ultrafiltration operating strategies achieving industrial specifications for recovery yield and mAb titer. A series of elution and filtration strategies are implemented, resulting in a total of 40 flowsheets explored. A steric mass action model with 7 pH-dependent parameters is employed to simulate scale-up polishing chromatography column behaviour. Moreover, a published gel layer ultrafiltration model based on Darcy’s law is implemented to purify the outlet of the polishing chromatography column. Strategies that are successful in achieving the target 90% recovery yield and 100 g L−1 concentration specifications are compared via a comprehensive analysis, to deduce the operational protocols which offer minimised salt consumption and pressure drop whilst respecting design criteria.

Indirect Electrochemical Cooling: Model-Based Performance Analysis and Working Fluid Selection.

The rising energy demand for cooling and heating requires efficient and sustainable technologies. Vapor-compression systems represent the state of the art but suffer from downscaling limits and maintenance needs. These disadvantages may be overcome by recently proposed electrochemical processes. However, their potential has not been explored systematically. This work quantifies the thermodynamic potential of an indirect electrochemical cooling process that replaces the vapor compressor of a standard refrigeration cycle with an electrochemical cell. An equilibrium-based process model evaluates the process performance of a working fluid, depending on its composition and temperatures in the process. After screening an extensive database for possible working fluids, an electrochemical cooling process is analyzed and optimized for the coefficient of performance (COP) to operate between two heat reservoirs at 20 °C (heat source) and 35 °C (heat sink). The majority of the investigated working fluids yield smaller or similar efficiencies than vapor-compression refrigeration, with COPs between 3.0 and 4.0. However, 35 promising working fluids that achieve higher efficiencies are identified with a COP up to 9.63, corresponding to 49% of Carnot. These working fluids are worthy of further investigation as their use in the electrochemical cooling process possibly outperforms standard vapor-compression refrigeration.

Comparing perfomance abstractions for collective adaptive systems

Non-functional properties of collective adaptive systems (CAS) are of paramount relevance practically in any application. This paper compares two recently proposed approaches to quantitative modelling that exploit different system abstractions: the first is based on generalised stochastic Petri nets, and the second is based on queueing networks. Through a case study involving autonomous robots, we analyse and discuss the relative merits of the approaches. This is done by considering three scenarios which differ on the architecture used to coordinate the distributed components. Our experimental results assess a high accuracy when comparing model-based performance analysis results derived from two different quantitative abstractions for CAS.

Towards efficient continuous thermally regenerative electrochemical cycle: Model-based performance map and analysis

Continuous thermally regenerative electrochemical cycle (TREC), which could convert heat to power and realize the continuous power output, is a promising technology for low-grade heat harvesting. However, the mechanism elucidation and performance analysis of continuous TREC are still incomplete, which hinders its performance improvement and practical applications. In this study, a pseudo-two-dimensional flow and electrochemical coupled model for continuous TREC is developed to investigate its performance. Based on the model, the polarization mechanism is revealed, and the effects of operating parameters and physical properties of electrolytes on the cycle performance are analyzed in detail. The results indicate that the concentration polarization is the major contributor to the system overpotential, especially at low electrolyte flow velocities. Regarding operating parameters, although temperatures of heat source and heat sink and electrolyte flow velocity determine the power density, the heat recuperation efficiency shows a decisive effect on the efficiencies. With perfect recuperation, the thermal efficiency and exergy efficiency could reach 8.3 % and 57.5 %, respectively. Regarding physical properties of electrolytes, the electrolyte concentrations affect the power density by changing the limiting current density. In addition, the temperature coefficient significantly affects the power density at any electrolyte flow velocity, while the internal resistance does so only at high flow velocities. With a temperature difference of 50 °C, a high power density of 4.33 W m−2 is expected if the temperature coefficient is increased to 4.0 mV K−1 and the area specific resistance is reduced to 1.0 Ω cm2. Based on the results, beneficial recommendations are put forward for further optimizing the system configuration and screening electrolytes for continuous TREC.

Performance analysis of an adaptive cooling system with primary and secondary heat paths for linear direct drives in machine tools

Machine tools subjected to high demands regarding productivity and accuracy are faced with the challenge that thermal losses influencing the accuracy negatively. Due to high requirements regarding thermal stability of precision related machine tool components, the focused linear direct drives (LDD) must be tempered by active cooling systems. In machine tools, a sufficient cooling capacity is available, but the cooling is insufficiently adjusted to the process and the individual demand of the heat-inducing as well as precision related components. With the intention to achieve a demand-oriented cooling, the use of thermoelectricity in machine tools is one research objective at the Institute for Machine Tools and Factory Management (IWF). Inspired by the concept of thermoelectric self-cooling (TSC)-systems for electronic devices, an Adaptive Cooling (AC)-system with thermoelectric generators (TEG) for LDD in machine tools is developed and experimentally investigated. In order to enhance the performance of AC-systems , in this research a reduction of the global thermal resistance is focused. A promising approach to achieve this goal is the division of the induced heat flow into a primary and a secondary heat path. For a model-based performance analysis of this approach, a system simulation is presented. To acquire experimental data for model validation, a test bench of the AC-system with primary as well as primary and secondary heat path is put into operation. The comparison of simulative and experimental determined data indicates a predominantly high model prediction accuracy. As a result, the implementation of a secondary heat path enables a reduction of the temperature on the upper surface of the heat source by 24.6% and thus a decrease of the global thermal resistance by 38.1%. Compared to the initial state of the AC-system only with primary heat path, the achieved thermal stability in the precision related machine tool component as well as the self-starting capability is improved.

Model-Based Performance Analysis of Membrane Reactor with Ethanol Steam Reforming over a Monolith.

Membrane reactors (MR) with an appropriate catalyst are considered to be an innovative and intensified technology for converting a fuel into the hydrogen-rich gas with the simultaneous recovery of high-quality hydrogen. Characteristics of an asymmetric membrane disk module consisting of a gas-tight nanocomposite functional coating (Ni + Cu/Nd5.5WO11.25-δ mixed proton-electron conducting nanocomposite) deposited on a gas-permeable functionally graded substrate has previously been extensively studied at lab-scale using MRs, containing the catalyst in a packed bed and in the form of a monolith. The catalytic monolith consisted of a FeCrAl substrate with a washcoat and an Ni + Ru/Pr0.35Ce0.35Zr0.35O2 active component. It has been shown that the driving potential for hydrogen permeation across the same membrane in a monolithic catalyst –assisted MR is greater compared to the packed bed catalyst. This paper presents results of the study where a one-dimensional isothermal model was used to interrelate catalytic and permeation phenomena in a MR with ethanol steam reforming over the monolith, operating at atmospheric pressure and in the temperature range of 700–900 °C. The developed mathematical reaction–transport model for the constituent layers of the catalyst-asymmetric membrane assembly together with a Sieverts’ equation for the functional dense layer, taking also into account the effect of boundary layers, was implemented in a COMSOL Multiphysics environment. Good agreement with the experimental data of the lab-scale MR with reasonable parameters values is provided. In numerical experiments, concentration profiles along the reactor axis were obtained, showing the effect of the emerging concentration gradient in the boundary layer adjacent to the membrane. Studies have shown that a MR with a catalytic monolith along with appropriate organization of a stagnant feed flow between the monolith and the membrane surface may enhance production and flux of hydrogen, as well as the efficiency characteristics of the reactor compared to a reactor with packed beds.

Mutation-based analysis of queueing network performance models

Performance models have been used in the past to understand the performance characteristics of software systems. However, the identification of performance criticalities is still an open challenge, since there might be several system components contributing to the overall system performance. This work combines two different areas of research to improve the process of interpreting model-based performance analysis results: (i) software performance engineering that provides the ground for the evaluation of the system’s performance; (ii) mutation-based techniques that nicely supports the experimentation of changes in performance models and contribute to a more systematic assessment of performance indices. We propose mutation operators for specific performance models, i.e., queueing networks, that resemble changes commonly made by designers when exploring the properties of a system’s performance. Our approach consists in introducing a mutation-based approach that generates a set of mutated queueing network models. The performance of these mutated networks is compared to that of the original network to better understand the effect of variations in the different components of the system. A set of benchmarks is adopted to show how the technique can be used to get a deeper understanding of the performance characteristics of software systems.

Automated model-based performance analysis of software product lines under uncertainty

Context: A Software Product Line (SPL) can express the variability of a system through the specification of configuration options. Evaluating performance characteristics, such as the system response time and resource utilization, of a software product is challenging, even more so in the presence of uncertain values of the attributes. Objective: The goal of this paper is to automate the generation of performance models for software products derived from the feature model by selection heuristics. We aim at obtaining model-based predictive results to quantify the correlation between the features, along with their uncertainties, and the system performance. This way, software engineers can be informed on the performance characteristics before implementing the system. Method: We propose a tool-supported framework that, starting from a feature model annotated with performance-related characteristics, derives Queueing Network (QN) performance models for all the products of the SPL. Model-based performance analysis is carried out on the models obtained by selecting the products that show the maximum and minimum performance-based costs. Results: We applied our approach to almost seven thousand feature models including more than one hundred and seventy features. The generation of QN models is automatically performed in much less than one second, whereas their model-based performance analysis embeds simulation delays and requires about six minutes on average. Conclusion: The experimental results confirm that our approach can be effective on a variety of systems for which software engineers may be provided with early insights on the system performance in reasonably short times. Software engineers are supported in the task of understanding the performance bounds that may encounter when (de)selecting different configuration options, along with their uncertainties.

PERFORMANCE ANALYSIS OF DIVIDING WALL COLUMN MODEL FOR FRACTIONATION OF OLEOCHEMICAL FATTY ACID

Fractionation process of oleochemical products in Malaysia normally uses conventional distillation columns (CDC). Due to the considerable amount of energy used in the separation process, there is a need to have an intensified process. Dividing wall column (DWC) is an attractive option which offers big advantages with reduced cost and energy consumption. However, research on its feasibility for oleochemical fractionation is scarce. In this article, we conducted a model-based performance analysis of DWC for oleochemical fatty acid fractionation. The model was designed and simulated using a customised four column configurations in Aspen Plus. A step-by-step design procedure was introduced to aid the model development. Economic and environmental assessment was performed and compared with conventional distillation columns. Hydraulic analysis of several packing type was also performed to gain insights on the column hydrodynamic behaviour. Our result shows that the major economic advantage is in the operating cost with savings of up to 16.9% and 10.2% on cooling and heating utilities, respectively while reduces 10.2% of CO2 emissions. Overall, our study shows the feasibility of fractionating fatty acids in oleochemical industry using DWC. Such assessment is important to assess its feasibility especially during the early stage of technology development prior to industrial implementation.

Model-based performance analysis of pleated filters with non-woven layers

The water flow rates of commercial sterilizing grade 10″ filter cartridges were simulated by Computational Fluid Dynamics (CFD) and compared to experimental data. This study compares four methods used to reconstruct the internal pleat geometry ranging from generic designs to analysis of microscopic images. The impact of the cartridges’ plastic cage on flow resistance was studied and found to be negligible. A systematic overestimation of the simulated filter flow rate was attributed to additional hydrodynamic resistance of the non-woven material between the pleats. The permeability of the non-woven material was estimated by fitting CFD models to experimentally determined water flow rates and compared to the permeability of this material as directly measured with a flow cell. Good correlation between CFD-based estimations and directly measured values was found at low pressures, while differences at high pressures indicated the existence of further flow resistance, which is hypothesized to be caused by deformation of the pleat geometry under pressure.

Analytic performance modeling and analysis of detailed neuron simulations

Big science initiatives are trying to reconstruct and model the brain by attempting to simulate brain tissue at larger scales and with increasingly more biological detail than previously thought possible. The exponential growth of parallel computer performance has been supporting these developments, and at the same time maintainers of neuroscientific simulation code have strived to optimally and efficiently exploit new hardware features. Current state-of-the-art software for the simulation of biological networks has so far been developed using performance engineering practices, but a thorough analysis and modeling of the computational and performance characteristics, especially in the case of morphologically detailed neuron simulations, is lacking. Other computational sciences have successfully used analytic performance engineering, which is based on “white-box,” that is, first-principles performance models, to gain insight on the computational properties of simulation kernels, aid developers in performance optimizations and eventually drive codesign efforts, but to our knowledge a model-based performance analysis of neuron simulations has not yet been conducted. We present a detailed study of the shared-memory performance of morphologically detailed neuron simulations based on the Execution-Cache-Memory performance model. We demonstrate that this model can deliver accurate predictions of the runtime of almost all the kernels that constitute the neuron models under investigation. The gained insight is used to identify the main governing mechanisms underlying performance bottlenecks in the simulation. The implications of this analysis on the optimization of neural simulation software and eventually codesign of future hardware architectures are discussed. In this sense, our work represents a valuable conceptual and quantitative contribution to understanding the performance properties of biological networks simulations.

Quasi-analytical model-based performance analysis of dual material gate stack strained GAA FinFET

ABSTRACT The evolution of traditional field effect transistor from planar to three-dimensional (3-D) device structure has led to higher package density and high current drive. However, due to continuous scaling, these non-planar devices (FinFET) have been found to suffer from performance degrading short channel effects (SCEs). In this work, a modified field effect device called dual material gate-all-around FinFET with High-K Gate-Oxide stack and strained channel is presented along with its quasi 3-D analytical model to evaluate the SCE immunity. This surface potential-based model includes gate work-function difference, characteristic gate length and equivalent oxide thickness. The channel potential is derived using quasi-3-D scaling equation where the characteristic length is derived using equivalent number of gates model. With the simple expression of potential distribution, threshold voltage and subthreshold swing have been obtained using minimum central potential. The device immunity to SCEs such as drain-induced barrier lowering, hot carriers and subthreshold degradation has also been examined for various structural and material modifications. The proposed analytical model has been verified using the 3-D numerical device simulator ATLAS.

Learning and statistical model checking of system response times

Since computers have become increasingly more powerful, users are less willing to accept slow responses of systems. Hence, performance testing is important for interactive systems. However, it is still challenging to test if a system provides acceptable performance or can satisfy certain response-time limits, especially for different usage scenarios. On the one hand, there are performance-testing techniques that require numerous costly tests of the system. On the other hand, model-based performance analysis methods have a doubtful model quality. Hence, we propose a combined method to mitigate these issues. We learn response-time distributions from test data in order to augment existing behavioral models with timing aspects. Then, we perform statistical model checking with the resulting model for a performance prediction. Finally, we test the accuracy of our prediction with hypotheses testing of the real system. Our method is implemented with a property-based testing tool with integrated statistical model checking algorithms. We demonstrate the feasibility of our techniques in an industrial case study with a web-service application.

Model-based performance analysis and scale-up of membrane adsorbers with a cassettes format designed for parallel operation

The performance of three membrane adsorber formats containing the same membrane type is comparatively analyzed. Membrane volumes range from 3 mL to 1.6 L, and above in parallel operation. Orthogonal modeling of internal flow patterns and binding properties is crucial for transferring model parameters across devices and scales. The binding rates are found to depend on the flow rate through convective transport in the macropores of the membrane. Breakthrough curves of large scale devices are successfully predicted using information acquired at smaller scales provided membrane properties, particularly binding capacity, are identical.

자율 무인잠수정의 모델기반 운동성능 해석

본 논문에서는 추진기 모델이 포함된 HAUV의 운동방정식을 사용한 모델기반 성능해석법이 제안되었다. 운동해석을 위해서 PD제어가 적용되었으며, MatLab/simulink로 프로그래밍 하였다. HAUV의 파라메터를 구하기 위해 ANSYS Fluent을 사용하였다. HAUV를 목표에 이동시키는 시뮬레이션에서 추진기의 응답성능 즉, 전류, RPM 및 추진력을 얻었다. 이상의 연구결과를 종합하여 HAUV의 기본성능인 운용속도, 배터리 가용시간, 운항거리 등을 예측하는 연구결과를 얻었다. 모델기반 운동해석은 부품의 설계성능이 모델에 포함되어 있으므로 설계요구조건이 변경되더라도 신속한 리모델링이 가능하다. 모델기반 운동성능 해석 기법은 시스템이나 부품의 요구조건이 변경될 경우에도 이에 대응한 강력한 설계 툴을 제공할 수 있다.

Energy model based Performance Analysis of cluster based wireless sensor network

Wireless sensor network is combination of large number of small sensor nodes, which are generally powered by battery. The nodes have limited battery energy and it is very difficult to replace easily due to their deployment scenarios and geographical constraints. This has made the energy consumption of the node as the main issue of wireless sensor network. The energy consumption is significant in finding the route in the network to pass on the data within the network. Most of the routing algorithms focus on the energy consumption path only. This paper presents the comparison of different energy models (Generic model, Mica-Z, Mica Motes) and analysis has been undertaken for performance of different metrics such as power consumed in transmit mode, receive mode, and idle mode and provide suitable answer to reduce the power consumption of the network. It is found that the proposed energy model in cluster based wireless sensor network Mica-motes model performance is better than other two energy models. The system has been implemented using Network Simulator Qualnet 5.2.

Simulation Model Based Performance Analysis of DSR and TORA Routing Protocols in MANET

Simulation Model Based Performance Analysis of DSR and TORA Routing Protocols in MANET

Model-based Development and Performance Analysis for Evolving Manufacturing Systems

Abstract Manufacturing systems and their control software exhibit a large number of variants, which evolve over time in order to meet changing functional and non-functional requirements. To handle the resulting complexity, we propose a multi-perspective modeling approach with different viewpoints regarding workflow, architecture and component behavior. We combine it with delta modeling to seamlessly capture variability and evolution by the same means on each of the viewpoints. We show how the separation in different viewpoints enables early performance analysis as well as code generation. The approach is illustrated using a case study.

A blueprint for system-level performance modeling of software-intensive embedded systems

Exploration of design alternatives and estimation of their key performance metrics such as latency and energy consumption is essential for making the proper design decisions in the early phases of system development. Often, high-level models of the dynamic behavior of the system are used for the analysis of design alternatives. Our work presents a blueprint for building efficient and re-usable models for this purpose. It builds on the well-known Y-chart pattern in that it gives more structure for the proper modeling of interaction on shared resources that plays a prominent role in software-intensive embedded systems. We show how the blueprint can be used to model a small yet illustrative example system with the Uppaal tool, and with the Java general-purpose programming language, and reflect on their respective strengths and weaknesses. The Java-based approach has resulted in a very flexible and fast discrete-event simulator with many re-usable components. It currently is used by TNO-ESI and Oce-Technologies B.V. for early model-based performance analysis that supports the design process for professional printing systems.

Aggregate Model-Based Performance Analysis of an Emergency Department

In this paper the authors present an aggregate simulation model for a real-life emergency department, which is based on the concept of effective process times and which uses a token system to model patients claiming multiple resources simultaneously. Although it has been developed for a specific hospital, the model is flexible, and capable to describe different settings. The modeling steps, model specification and model validation are explained in detail. By using a process-based simulation language, the resulting model is transparent, intuitive and easy to use in quantitatively and efficiently evaluating the effect of proposed changes in the operational processes of the emergency department on patient service levels and resource utilizations.